The Need for Machine-Processable Agreements in Health Data Management

Konstantinidis, George; Chapman, Adriane; Weal, Mark J.; Alzubaidi, Ahmed; Ballard, Lisa; Lucassen, Anneke

doi:10.3390/a13040087

Cited by 3 publications

(3 citation statements)

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“…Conjunctive queries are already employed as a privacy policy language in several areas [10,23,36]. Many future directions for this research open up, some of which we are already working on: bringing this language closer to the end-user [32], investigate the benefits of our alternative annotation-based implementation, investigate interaction with reasoning constraints, precisely formalize aggregation, and finally look into how we can support a service provider to enforce consent across multiple queries, in essence, "propagate" consent by inducing more consent constraints relative to a query answer for later use; we envisage the latter happening with a queryanswering-using-views technique or similar [30,31].…”

Section: Discussionmentioning

confidence: 99%

“…While in Section 4 we develop an algorithm to implement our semantics without relying on annotations, using annotations for our semantics allows for a more tangible interpretation of what our constraints mean since they describe particular tuples; as such, a front-end implementation could even support a visually-aided way of denoting the constraints similar to Figure 1. There is ongoing research [7,32] as well as real and emerging systems [23,42] that help transform user policies into CQs. In this paper we focus on the foundational and algorithmic aspects of consent abiding-query answering, rather than the interface.…”

Section: From Adversarial To Collaborative Privacymentioning

confidence: 99%

“…This unioning essentially consists of comparing all equivalence classes in the two sets and merging them if they contain common values; or fail if they contain common values but their representatives are different constants. At the end of this process we apply the most general query unifiers to get their query unifications, and we compute equalities between terms that we will use as 𝐽 𝐴 and 𝐽 𝜃 (lines [27][28][29][30][31][32].…”

Section: Algorithmmentioning

confidence: 99%

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Enabling personal consent in databases

2021

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Users have the right to consent to the use of their data, but current methods are limited to very coarse-grained expressions of consent, as "opt-in/opt-out" choices for certain uses. In this paper we identify the need for fine-grained consent management and formalize how to express and manage user consent and personal contracts of data usage in relational databases. Unlike privacy approaches, our focus is not on preserving confidentiality against an adversary, but rather cooperate with a trusted service provider to abide by user preferences in an algorithmic way. Our approach enables data owners to express the intended data usage in formal specifications, that we call consent constraints , and enables a service provider that wants to honor these constraints, to automatically do so by filtering query results that violate consent; rather than both sides relying on "terms of use" agreements written in natural language. We provide formal foundations (based on provenance), algorithms (based on unification and query rewriting), connections to data privacy, and complexity results for supporting consent in databases. We implement our framework in an open source RDBMS, and provide an evaluation against the most relevant privacy approach using the TPC-H benchmark, and on a real dataset of ICU data.

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Section: Discussionmentioning

confidence: 99%

Section: From Adversarial To Collaborative Privacymentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

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Enabling personal consent in databases

2021

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Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Dambha‐Miller¹,

Simpson²,

Akyea³

et al. 2022

JMIR Res Protoc

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Background Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. Objective We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. Results The study will commence in October 2021 and is expected to be completed by October 2023. Conclusions By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers “whole persons” and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M. International Registered Report Identifier (IRRID) PRR1-10.2196/34405

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Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions) (Preprint)

Dambha‐Miller¹,

Simpson²,

Akyea³

et al. 2021

Preprint

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BACKGROUND Multiple long-term health conditions (Multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality and health-care expenditure. Strategies to tackle this have primarily focused on addressing biological aspects of disease, but MLTC-M are also the result of and associated with additional psycho-social, economic and environmental barriers. A shift towards more personalised, holistic and integrated care could be effective. This could be made more efficient by identifying groups of the population based on their health and social need. These will, in turn, contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social need and to quantify the impact of clusters on long-term health and costs. OBJECTIVE To develop and validate population clusters that consider determinants of health and social care need for people with MLTC-M using data-driven ML(machine-learning) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. METHODS A mixed-methods programme of work with parallel work streams including; 1) Qualitative semi-structured interview study exploring patient, carer and professional views on clinical and socio-economic factors influencing experiences of living with, or seeking care in MLTC-M, 2) Modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine feasibility of including these variables within existing primary care databases and 3) Cohort study with expert driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterised and trajectories over time examined, to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity and ten-year health/social care costs. RESULTS The study will commence in October 2021 and is expected to be completed by October 2023. CONCLUSIONS By studying MLTC-M clusters we will assess how more personalised care can be developed, accurate costs provided, better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers the ‘whole person’ and their environment is essential in addressing the complex, diverse and individual needs of people living with MLTC-M. CLINICALTRIAL Not applicable

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The Need for Machine-Processable Agreements in Health Data Management

Cited by 3 publications

References 32 publications

Enabling personal consent in databases

Enabling personal consent in databases

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions) (Preprint)

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